search for: y_hat

Hello, suppose one has the following values x1 <- rnorm(10,5,1) x2 <- rgamma(10,5,1) y <- rnorm(10,4,1) mydat <- data.frame(y,x1,x2) then one can use glm like mod <- glm(y~x1+x2, data=mydat, family=gaussian) But how could I estimate y_hat? Thanks alot! Sam --------------------------------- [[alternative HTML version deleted]]

Hello Can anyone tell me what I am doing wrong below? My Y and y_hat are the same. A<-scale(stackloss) n1<- dim(A)[1];n2<-dim(A)[2] X<-svd(A) Y<- matrix(A[,"stack.loss"],nrow=n1) Y y_hat <-matrix((X$u%*% t(X$u))%*%Y,nrow=n1,byrow=T) y_hat [[alternative HTML version deleted]]

Hi, I try to execute the seven lines of code below to plot a graph. But I am failing as the messages below show. Where am I going wrong? install.packages("rgl") library(rgl) y_hat = X%*%B_hat open3d(windowRect = c(100,100,900,900),family = "serif") color = rainbow(length(y_hat))[rank(y_hat)] plot3d(educ,exper,wage,col = color,type = "s",size = 0.5,xlim = c(0,20),ylim = c(0,60),zlim = c(-10,70),box = FALSE,axes = TRUE) planes3d(B_hat[2],B_hat[3],-1,B_hat[1...

...=rnorm(10,mean=5) x1=rnorm(10,mean=2) x2=rnorm(10) lin=lm(y~x1+x2) summary(lin) ## In the summary, 'Residual standard error: 1.017 on 7 degrees of freedom', 1.017 is the estimate of the constance variance? Q2: beta0=lin$coefficients[1] beta1=lin$coefficients[2] beta2=lin$coefficients[3] y_hat=beta0+beta1*x1+beta2*x2 ## Is there any built-in function in R to obtain y_hat directly? Q3: If I want to apply this regression result to another dataset, that is, new x1 and x2. Is the built-in function in 2 still available? Thank you in advance! [[alternative HTML version deleted]]

...hlf, p. 465/471 n <- length(x) mydf <- n-2 ## least square fit: x2 <- (x-mean(x))^2 y2 <- (y-mean(y))^2 ## regression (pedestrian solution): xy <- (x-mean(x))*(y-mean(y)) slope1 <- sum(xy)/sum(x2) intercept_a <- mean(y) - slope1 * mean(x) ## model data y_hat: y_hat <- intercept_a + slope1 * x ## least squares of model data: y_hat2 <- (y - y_hat)^2 s2yx <- sum(y_hat2) / (n-2) sb <- sqrt(s2yx/sum(x2)) ts <- (slope1 - slope_2) / sb pvalue <- 2*(pt(abs(ts), df, lower.tail=FALSE)) ## 0.95 for one-tailed 0.975 for two-tai...

...#39;ve gotten used to using the svm function in the e1071 package. It works great. Now, I want to do/learn some more interesting stuff. (Perhaps my own kernel and/or scoring system). So I want to better understand 1) how calculation of the kernel happens. 2) how to calculate the predicted value (y_hat) given a list of support vectors and coefficients. I've seen all the formulas and many of the books. I get most of it conceptually. Where I'm having trouble is making the leap from concept to actual use. Ideally, I'd love to code some of the basic stuff in R or Perl in scratch. It...

...I apologize if it is too simple question for all. I have a multivariate dataset having 7 variables as independent and 1 dependent variable. 248 data points are there. I want to do out sample forecast first considering 156 points. So I'll have to start from 157th point and calculate the 157th y_hat value. In this way it will go to 248th data point. Can any one tell me how I can do with for loop. Thanks a lot in advance. Thanks & Regards, SUMANTA BASAK. ------------------------------------------------------------------------------------------------------------------- This e-mail...

...1 2 0.2708064 0.7254615 predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="prediction")$se.fit 1 2 0.2708064 0.7254615 I was surprised to find that the standard errors returned were in fact the standard errors of the sampling distribution of Y_hat: sqrt(MSE(1/n + (x-x_bar)^2/SS_x)), not the standard errors of Y_new (predicted value): sqrt(MSE(1 + 1/n + (x-x_bar)^2/SS_x)). Is there a reason this quantity is called the "standard error of predicted means" if it doesn't relate to the prediction distribution? Turning to Neter e...

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients